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How to generate a random data vector that follows a constraint

Asked by Saifullah Khalid on 29 Sep 2019
Latest activity Commented on by Saifullah Khalid on 30 Sep 2019
I want to generate random data vector where that follows this constrainst. where and is a constant. I would appreciate any help.


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2 Answers

Answer by Bruno Luong
on 29 Sep 2019
Edited by Bruno Luong
on 29 Sep 2019
 Accepted Answer

This will generate uniform distribution (within to polytope)
N = 10;
Etotal = 0;
E = -log(rand(N+1,1));
E = E(1:N,:)./sum(E,1);


Here is a comparison of distribution with the two other methods proposed below to show the issue of non-uniformity if one doesn't pay attention
N = 2;
Etotal = 1;
p = 3e3;
Ebias = rand(1,p) .* randfixedsum(N,p,Etotal,0,Etotal);
E = rand(1,p).^(1/N)*Etotal .* randfixedsum(N,p,1,0,1);
E2 = -log(rand(N+1,p)); E2 = E2(1:N,:)./sum(E2,1);
title('rand * randfixedsum')
axis equal
axis equal
title('sqrt(rand) * randfixedsum')
axis equal
title('exponential method')
Bruno Luong, thank very much for detailed answer and the insight.

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Answer by Walter Roberson
on 29 Sep 2019

Look in the File Exchange for Roger's randfixedsum(). Generate a vector with fixed sum . Multiply all of the elements by rand() to implement the <= part. (Though you might want to worry about the difficulty that rand() is never exactly 1, so if you generate a sum exactly equal to and multiply by rand() then the result can never exactly total


IIUC you propose
E = rand()*Etotal * randfixedsum(N,1,1,0,1)
this will generate bad distribution (too many point close to 0 compare to uniform distribution), espetially for N >> 1.
I was thinking of
rand() * randfixedsum(N,1,Etotal,0,Etotal)
but your comment might still apply.
Both give the same non-uniform pdf
The "correct" one is
E = rand()^(1/N)*Etotal * randfixedsum(N,1,1,0,1);

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